Boundary distortion point cloud optimization extraction method for curved surface parts

Abstract

A boundary distortion point cloud optimization extraction method for curved surface parts comprises the following steps: 1) firstly, scanning point clouds of the curved surface parts containing boundary features to establish a grid structure, dividing the point clouds into a region containing the boundary features and a region not containing the boundary features according to grid center curvature, and simplifying the point clouds; (2) normal statistics is carried out on the simplified boundary feature points, weighted average is used for carrying out smoothing processing on a normal line, a plane is fitted, position optimization is carried out on points which are too far away from the fitted plane, and therefore smoothing of the boundary features is completed; and 3) performing curved surface fitting on the optimized point cloud surface patch by using a parameterization method, and intersecting and cutting the obtained reconstructed curved surface to obtain an actual measurement model with clear boundary characteristics. According to the invention, for the characteristic of curved surface part scanning point cloud boundary distortion, the accuracy of actual measurement of model boundary features is improved.

Description

Technical Field

[0001] The present invention relates to the field of measured model construction, and more specifically to a method for accurately constructing a measured model with a clear and sharp boundary by optimizing the boundary point cloud of a curved surface part with a clear and sharp boundary (without chamfer) to solve the boundary distortion problem. Background Art

[0002] In the field of measuring models of curved parts with clear sharp boundaries (no chamfers), 3D scanning technology is widely used to obtain geometric information of the surface of objects. Through 3D scanning equipment, point cloud data of the target object can be quickly generated, and then an accurate 3D model can be constructed. However, when dealing with structures with clear sharp boundaries (no chamfers), existing scanning technologies and point cloud processing methods often face some challenges.

[0003] First, due to the working principle and measurement errors of 3D scanning equipment, the scanned point cloud data often show distortion in the area of ​​sharp boundaries. This distortion may appear as blurred boundaries, unclear edges or additional noise points, thus affecting the accuracy and quality of the final model. Secondly, although traditional point cloud processing algorithms, such as smoothing filters and surface fitting methods, can improve the overall smoothness of the point cloud, they often lead to the loss or deformation of boundary features when processing sharp boundaries, making the reconstructed model unable to accurately reflect the details of the original structure.

[0004] In addition, for application scenarios that require high-precision models, such as industrial design, reverse engineering, and cultural heritage protection, maintaining the sharp boundaries of the structure is crucial. Therefore, how to effectively optimize and restore the sharp boundaries in the scanned point cloud while ensuring the overall model quality has become a technical problem that needs to be solved urgently.

[0005] In summary, the existing technology has obvious shortcomings when processing structures with clear sharp boundaries, and a new method is urgently needed to optimize the boundary point cloud to obtain a measured model with clear and sharp boundaries. Summary of the invention

[0006] The purpose of the present invention is to solve the problem of boundary distortion of scanning point clouds of parts with clear and sharp boundary surfaces, and to invent a boundary distortion point cloud optimization extraction method for surface parts. It optimizes the boundary point cloud to construct a high-precision measured model with clear and sharp boundaries.

[0007] The technical solution of the present invention is:

[0008] A method for optimizing and extracting boundary distortion point clouds for curved surface parts, characterized in that the method for constructing a complex structure measured model based on boundary optimization comprises the following steps:

[0009] S1: First, a grid structure is established for the scanned point cloud, and the point cloud is divided into regions with boundary features and regions without boundary features according to the central curvature of the grid and simplified;

[0010] S2: Perform normal statistics on the simplified boundary feature points, use weighted average to smooth the normals and fit the plane, optimize the positions of points that are too far from the fitting plane, and complete the smoothing of the boundary features;

[0011] S3: Use the parametric method to fit the optimized point cloud patch to the surface, make a topological consistency judgment on the reconstructed surface, and cut it to obtain a measured model with clear boundary features.

[0012] In step S1, a spatial grid is established for the scanned point cloud, and the point cloud is divided into a region with boundary features and a region without boundary features according to the central curvature of the grid, and the following steps are simplified:

[0013] S1.1: Create a spatial grid and set the edge value of the point cloud data x, y, and z to x max 、x min ,y max ,y min 、z max 、z min , determine the grid size d according to the resolution of the scanning device, and use (x min -d / 2,y min -d / 2,z min -d / 2) as the initial point of the grid and extend the grid to the maximum point (x max ,y max , z max ) is included in the termination, dividing the point cloud into N x ×N y ×N z In the grid

[0014]

[0015] Among them, ┌﹒┐ means rounding up. Use n x ∈(0, Nx-1), n x ∈(0, Nx-1), n x ∈(0, Nx-1), p∈{0,1} is used to number each grid, and the grid coordinates (n x , n y , n z , p) indicates the starting point is (n x d, n y d, n z d) A grid with a side length of d. The point cloud data is placed in the corresponding grid. If the point exists in the grid, p = 1, otherwise p = 0.

[0016] S1.2: Use the quadratic surface fitting method to calculate the average curvature of the center point of the grid structure, and set the curvature threshold to determine whether further subdivision is needed. The specific subdivision method is as follows:

[0017] ① Calculate the average curvature of all point clouds in the grid and determine the threshold H based on the curvature distribution t ;

[0018] ② The mean curvature H i >H t The grid is further subdivided, and the length of the grid structure after subdivision is d / 2;

[0019] ③ Re-divide the point cloud in the original grid into the subdivided grid according to the spatial distribution.

[0020] S1.3: In step S1.2, there are grids of length d and d / 2. The point cloud in the grid of length d does not contain the boundary area; the point cloud in the spatial grid of length d / 2 contains the boundary area. Different simplification strategies are used for the two grid point clouds:

[0021] ① For points in the large grid, use the grid center as the simplified point cloud;

[0022] ② The points in the small grid use a simplified method based on curvature to calculate the average curvature of the grid center point in the neighborhood, and sample the grid at a rate ρ according to the curvature. i A random sample of j =H j / H max .

[0023] In step S2, normal statistics are performed on the simplified boundary feature points, the normals are smoothed using weighted average and the plane is fitted, and the positions of the points that are too far from the fitting plane are optimized, thereby completing the smoothing of the boundary features, which includes the following steps:

[0024] S2.1: Use the gradient method to detect clear boundary lines from the point cloud containing the boundary area. The steps are as follows:

[0025] ①Define the neighborhood radius: Use a fixed radius spherical neighborhood and define the neighborhood of point p as the set of all points whose distance to p is less than or equal to a fixed radius r.

[0026] N(p)={q|||pq||≤r} (2)

[0027] ②Calculate the gradient size G(p) of point p

[0028]

[0029] Where ω(q) is the weight function, n p、n q are the normal vectors of point p and point q respectively.

[0030] ③Set threshold G T , the gradient is greater than G T The points are retained in set C

[0031] C={pi|G p >G T} (4)

[0032] ④ Extract points greater than the gradient threshold, use non-maximum suppression, retain the points with the largest gradient in the neighborhood, and connect them into boundary feature lines

[0033] G(p)=max{G(q)|q∈N(p)∩C} (5)

[0034] S2.2: Use the normal-guided method to optimize the boundary and nearby point clouds. The optimization process is divided into two steps:

[0035] ① Use the weighted average normal to smooth the normal. For the vertex p in the boundary area i , calculate the weighted average of the normals of other points in the neighborhood to get the smooth normal n i ˊ

[0036]

[0037] For vertex p i , select the angle consistency threshold θ t and the distance consistency threshold r t , check whether the smooth normal of the neighboring point is consistent with its own smooth normal n i The angle between ˊ is less than θ t And the distance is less than r t :

[0038]

[0039] ② For the point cloud data after normal smoothing, use An i ˊ+b represents the fitted plane, and the fitted plane P is obtained by solving the least squares problem. m

[0040] n′ T x+d m =0 (8)

[0041] where d m is the offset, calculate the point to plane P m The distance D im , for the value exceeding the threshold d t Points are translated onto the surface along the smoothed normal

[0042]

[0043] In step S3, the optimized point cloud patch is subjected to surface fitting using a parameterized method, and the obtained reconstructed surface is intersected and clipped to obtain a measured model with clear boundary features, which includes the following steps:

[0044] S3.1: After obtaining the optimized boundary, segmentation based on the boundary can obtain accurate surface features, and a parameterized fitting method is adopted for the surface.

[0045] S3.2: The surface features reconstructed from point cloud fitting are only single surface features, and there are intersections, so post-processing is required to obtain the measured model. Post-processing mainly includes intersection processing based on the topological relationship of the surface features. The obtained intersection line is the boundary feature of the measured model. It is also necessary to retain or remove the facets divided by the diagonal lines according to their directions. Finally, to ensure that the generated measured model is a complete geometric model, it is judged for its closure. The steps are as follows:

[0046] ① First, the topological relationship of the two intersecting reconstructed surfaces S1 and S2 is judged and the intersection is calculated. The obtained intersection line L1 is the edge line of the measured model;

[0047] ② According to the edge line L1, S1 is divided into two parts after the intersection, and S2 is divided into two parts after the intersection. Project the reference points of S1 and S2 to the nearest reconstructed surface, retain the projection area, delete other areas, and complete the surface trimming.

[0048] ③Finally, the integrity of the measured model is judged by judging each reconstructed untrimmed face. If the edge features after trimming are closed, it means that the measured model is a complete geometric model.

[0049] The beneficial effects of the present invention are:

[0050] 1. Improve model accuracy: By optimizing the boundary point cloud, the boundary accuracy of the measured model can be significantly improved, ensuring the clarity and accuracy of sharp edges, thereby providing a higher quality 3D model.

[0051] 2. Enhanced detail retention: The method of the present invention can accurately retain the sharp boundary features of the structure while maintaining the overall smoothness, avoiding the boundary blur or deformation caused by traditional smoothing algorithms.

[0052] 3. Improve the breadth of application: Since the optimized model has higher accuracy and better detail retention, it is suitable for a variety of application scenarios with high precision requirements, such as industrial design, reverse engineering, cultural heritage protection, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of the method for optimizing the extraction of boundary distortion point clouds for curved surface parts in the present invention.

[0054] Figure 2 It is a schematic diagram of the extraction of boundary point cloud of parts containing boundary feature surfaces in the present invention.

[0055] Figure 3 It is a schematic diagram of the boundary optimization results of parts containing boundary feature surfaces in the present invention.

[0056] Figure 4 It is a schematic diagram of the surface fitting, cutting and splicing of parts containing boundary feature surfaces in the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings and implementation methods.

[0058] like Figure 1-4 shown.

[0059] A method for optimizing the extraction of boundary distortion point clouds for curved surface parts. The process is as follows: Figure 1 As shown, the specific steps are as follows:

[0060] S1: First, a grid structure is established for the scanned point cloud, and the point cloud is divided into regions with boundary features and regions without boundary features according to the central curvature of the grid and simplified;

[0061] S2: Perform normal statistics on the simplified boundary feature points, use weighted average to smooth the normals and fit the plane, optimize the positions of points that are too far from the fitting plane, and complete the smoothing of the boundary features;

[0062] S3: Use the parametric method to fit the optimized point cloud patch to the surface, make a topological consistency judgment on the reconstructed surface, and cut it to obtain a measured model with clear boundary features.

[0063] The detailed description is as follows:

[0064] In step S1, a spatial grid is established for the scanned point cloud, and the point cloud is divided into a region with boundary features and a region without boundary features according to the central curvature of the grid, and the following steps are simplified:

[0065] S1.1: Create a spatial grid and set the edge value of the point cloud data x, y, and z to x max 、x min ,y max ,y min 、z max 、z min , determine the grid size d according to the resolution of the scanning device, and use (xmin -d / 2,y min -d / 2,z min -d / 2) as the initial point of the grid and extend the grid to the maximum point (x max ,y max , z max ) is included in the termination, dividing the point cloud into N x ×N y ×N z In the grid

[0066]

[0067] Among them, ┌﹒┐ means rounding up. Use n x ∈(0, Nx-1), n x ∈(0, Nx-1), n x ∈(0, Nx-1), p∈{0,1} is used to number each grid, and the grid coordinates (n x , n y , n z , p) indicates the starting point is (n x d, n y d, n z d) A grid with a side length of d. The point cloud data is placed in the corresponding grid. If the point exists in the grid, p = 1, otherwise p = 0.

[0068] S1.2: Use the quadratic surface fitting method to calculate the average curvature of the center point of the grid structure, and set the curvature threshold to determine whether further subdivision is needed. The specific subdivision method is as follows:

[0069] ① Calculate the average curvature of all point clouds in the grid and determine the threshold H based on the curvature distribution t ;

[0070] ② The mean curvature H i >H t The grid is further subdivided, and the length of the grid structure after subdivision is d / 2;

[0071] ③ Re-divide the point cloud in the original grid into the subdivided grid according to the spatial distribution.

[0072] The extracted boundary point cloud is divided into small voxel grids. The specific divided point cloud is as follows Figure 2 shown.

[0073] S1.3: In step S1.2, there are grids of length d and d / 2. The point cloud in the grid of length d does not contain the boundary area; the point cloud in the spatial grid of length d / 2 contains the boundary area. Different simplification strategies are used for the two grid point clouds:

[0074] ① For points in the large grid, use the grid center as the simplified point cloud;

[0075] ② The points in the small grid use a simplified method based on curvature to calculate the average curvature of the grid center point in the neighborhood, and sample the grid at a rate ρ according to the curvature. i A random sample of j =H j / H max .

[0076] In step S2, normal statistics are performed on the simplified boundary feature points, the normals are smoothed using weighted average and the plane is fitted, and the positions of the points that are too far from the fitting plane are optimized, thereby completing the smoothing of the boundary features, which includes the following steps:

[0077] S2.1: Use the gradient method to detect clear boundary lines from the point cloud containing the boundary area. The steps are as follows:

[0078] ①Define the neighborhood radius: Use a fixed radius spherical neighborhood and define the neighborhood of point p as the set of all points whose distance to p is less than or equal to a fixed radius r.

[0079] N(p)={q|||pq||≤r} (2)

[0080] ②Calculate the gradient size G(p) of point p

[0081]

[0082] Where ω(q) is the weight function, n p 、n q are the normal vectors of point p and point q respectively.

[0083] ③Set threshold G T , the gradient is greater than G T The points are retained in set C

[0084] C={pi|G p >G T} (4)

[0085] ④ Extract points greater than the gradient threshold, use non-maximum suppression, retain the points with the largest gradient in the neighborhood, and connect them into boundary feature lines

[0086] G(p)=max{G(q)|q∈N(p)∩C} (5)

[0087] S2.2: Use the normal-guided method to optimize the boundary and nearby point clouds. The optimization process is divided into two steps:

[0088] ① Use the weighted average normal to smooth the normal. For the vertex p in the boundary areai , calculate the weighted average of the normals of other points in the neighborhood to get the smooth normal n i ˊ

[0089]

[0090] For vertex p i , select the angle consistency threshold θ t and the distance consistency threshold r t , check whether the smooth normal of the neighboring point is consistent with its own smooth normal n i The angle between ˊ is less than θ t And the distance is less than r t :

[0091]

[0092] ② For the point cloud data after normal smoothing, use An i ˊ+b represents the fitted plane, and the fitted plane P is obtained by solving the least squares problem. m

[0093] n′ T x+d m =0 (8)

[0094] where d m is the offset, calculate the point to plane P m The distance D im , for the value exceeding the threshold d t Points are translated onto the surface along the smoothed normal

[0095]

[0096] The point cloud mesh obtained by boundary optimization is as follows Figure 3 shown.

[0097] In step S3, the optimized point cloud patch is subjected to surface fitting using a parameterized method, and the obtained reconstructed surface is intersected and clipped to obtain a measured model with clear boundary features, which includes the following steps:

[0098] S3.1: After obtaining the optimized boundary, segmentation based on the boundary can obtain accurate surface features, and a parameterized fitting method is adopted for the surface.

[0099] S3.2: The surface features obtained by point cloud fitting and reconstruction are only single surface features, and there are intersections, so post-processing is required to obtain the measured model. Post-processing mainly includes intersection processing based on the topological relationship of the surface features. The obtained intersection line is the boundary feature of the measured model. It is also necessary to retain or remove the facets divided by the diagonal lines according to their directions. Finally, to ensure that the generated measured model is a complete geometric model, it is judged for its closure. The steps are as follows:

[0100] ① First, Figure 4 The topological relationship of the two intersecting reconstructed surfaces S1 and S2 is judged and the intersection is calculated, and the obtained intersection line L1 is the edge line of the measured model;

[0101] ② According to the edge line L1, S1 is divided into two parts ① and ② after the intersection, and S2 is divided into two parts ③ and ④ after the intersection. Project the reference points of S1 and S2 to the nearest reconstructed surface, retain the projection area, delete other areas, and complete the surface trimming.

[0102] ③Finally, the integrity of the measured model is judged by judging each reconstructed untrimmed face. If the edge features after trimming are closed, it means that the measured model is a complete geometric model.

[0103] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0104] The present invention does not involve parts that are the same as the prior art or can be implemented by using the prior art.

Claims

1. A method for optimizing the extraction of boundary distortion point clouds for curved surface parts, characterized by: The steps of optimizing, extracting and reconstructing the boundary distortion point cloud of the surface part are as follows: S1: First, a grid structure is established for the scanned point cloud, and the point cloud is divided into regions with boundary features and regions without boundary features according to the central curvature of the grid and simplified; S2: Perform normal statistics on the simplified boundary feature points, use weighted average to smooth the normals and fit the plane, optimize the positions of points that are too far from the fitting plane, and complete the smoothing of the boundary features; S3: Use the parametric method to fit the optimized point cloud patch to the surface, make a topological consistency judgment on the reconstructed surface, and cut it to obtain a measured model with clear boundary features.

2. The method according to claim 1, characterized in that: In step S1, a spatial grid is established for the scanned point cloud, and the point cloud is divided into a region with boundary features and a region without boundary features according to the central curvature of the grid, and the following steps are simplified: S1.1: Create a spatial grid and set the edge value of the point cloud data x, y, and z to x max 、x min ,y max ,y min 、z max 、z min , determine the grid size d according to the resolution of the scanning device, and use (x min -d / 2,y min -d / 2,z min -d / 2) as the initial point of the grid and extend the grid to the maximum point (x max ,y max , z max ) is included in the termination, dividing the point cloud into N x ×N y ×N z In the grid Among them, ┌﹒┐ is rounded up; use n x ∈(0, Nx-1), n x ∈(0, Nx-1), n x ∈(0, Nx-1), p∈{0,1} is used to number each grid, and the grid coordinates (n x , n y , n z , p) indicates the starting point is (n x d, n y d, n z d) A grid with a side length of d. Put the point cloud data into the corresponding grid. If there is a point in the grid, p = 1, otherwise p = 0; S1.2: Use the quadratic surface fitting method to calculate the average curvature of the center point of the grid structure, and set the curvature threshold to determine whether further subdivision is needed. The specific subdivision method is as follows: ① Calculate the average curvature of all point clouds in the grid and determine the threshold H based on the curvature distribution t ; ② The mean curvature H i >H t The grid is further subdivided, and the length of the grid structure after subdivision is d / 2; ③ Re-divide the point cloud in the original grid into the subdivided grid according to the spatial distribution. S1.3: In step S1.2, there are grids of length d and d / 2. The point cloud in the grid of length d does not contain the boundary area; the point cloud in the spatial grid of length d / 2 contains the boundary area. Different simplification strategies are used for the two grid point clouds: ① For points in the large grid, use the grid center as the simplified point cloud; ② The points in the small grid use a simplified method based on curvature to calculate the average curvature of the grid center point in the neighborhood, and sample the grid at a rate ρ according to the curvature. i A random sample of j =H j / H max .

3. The method according to claim 1, characterized in that: In step S2, normal statistics are performed on the simplified boundary feature points, and the normals are smoothed using weighted average and fitted to the plane, and the positions of the points that are too far from the fitting plane are optimized, thereby completing the smoothing of the boundary features. The following steps are involved: S2.1: Use the gradient method to detect clear boundary lines from the point cloud containing the boundary area. The steps are as follows: ①Define the neighborhood radius: Use a fixed radius spherical neighborhood and define the neighborhood of point p as the set of all points whose distance p is less than or equal to a fixed radius r: N(p)={q|||pq||≤r} (2) ② Calculate the gradient size G(p) of point p: Where ω(q) is the weight function, n p 、n q are the normal vectors of point p and point q respectively; ③Set threshold G T , the gradient is greater than G T The points are retained in set C: C={pi|G p >G T } (4) ④ Extract points greater than the gradient threshold, use non-maximum suppression, retain the points with the largest gradient in the neighborhood, and connect them into boundary feature lines: G(p)=max{G(q)|q∈N(p)∩C} (5) S2.2: Use the normal-guided method to optimize the boundary and nearby point clouds. The optimization process is divided into two steps: ① Use the weighted average normal to smooth the normal. For the vertex p in the boundary area i , calculate the weighted average of the normals of other points in the neighborhood to get the smooth normal n i ˊ: For vertex p i , select the angle consistency threshold θ t and the distance consistency threshold r t , check whether the smooth normal of the neighboring point is consistent with its own smooth normal n i The angle between ˊ is less than θ t And the distance is less than r t : ② For the point cloud data after normal smoothing, use An i ˊ+b represents the fitted plane, and the fitted plane P is obtained by solving the least squares problem. m : n′ T x+d m =0 (8) where d m is the offset, calculate the point to plane P m The distance D im , for the value exceeding the threshold d t , and translate it onto the surface along the smoothed normal:

4. The method according to claim 1, characterized in that: In step S3, the optimized point cloud patch is subjected to surface fitting using a parameterized method, and the obtained reconstructed surface is intersected and clipped to obtain a measured model with clear boundary features, which includes the following steps: S3.1: After obtaining the optimized boundary, segmentation based on the boundary can obtain accurate surface features, and a parameterized fitting method is adopted for the surface; S3.2: The surface features reconstructed from point cloud fitting are only single surface features, and there are intersections. Post-processing is required to obtain the measured model. Post-processing mainly includes intersection processing based on the topological relationship of the surface features. The obtained intersection line is the boundary feature of the measured model. It is also necessary to retain or remove the facets divided by the diagonal lines according to their directions. Finally, to ensure that the generated measured model is a complete geometric model, the closedness is judged. The steps are as follows: ① First, the topological relationship of the two intersecting reconstructed surfaces S1 and S2 is judged and the intersection is calculated. The obtained intersection line L1 is the edge line of the measured model; ② According to the edge line L1, S1 is divided into two parts after the intersection, and S2 is divided into two parts after the intersection. Project the reference points of S1 and S2 to the nearest reconstructed surface, retain the projection area, delete other areas, and complete the surface trimming; ③Finally, the integrity of the measured model is judged by judging each reconstructed untrimmed face. If the edge features after trimming are closed, it means that the measured model is a complete geometric model.

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